(See Getting Started with SFrames for setup instructions)
import graphlab
song_data = graphlab.SFrame('song_data.gl/')
Music data shows how many times a user listened to a song, as well as the details of the song.
song_data.head()
graphlab.canvas.set_target('ipynb')
song_data['song'].show()
len(song_data)
users = song_data['user_id'].unique()
len(users)
song_data.groupby(key_columns='artist', operations={'unique_users': graphlab.aggregate.COUNT_DISTINCT('user_id')}).sort(
['unique_users'])
data[(data['artist'] == "Kanye West") | (data['artist'] == "Lady GaGa") | (data['artist'] == "Foo Fighters")
| (data['artist'] == "Taylor Swift")]
train_data,test_data = song_data.random_split(.8,seed=0)
popularity_model = graphlab.popularity_recommender.create(train_data,
user_id='user_id',
item_id='song')
A popularity model makes the same prediction for all users, so provides no personalization.
popularity_model.recommend(users=[users[0]])
popularity_model.recommend(users=[users[1]])
We now create a model that allows us to make personalized recommendations to each user.
personalized_model = graphlab.item_similarity_recommender.create(train_data,
user_id='user_id',
item_id='song')
As you can see, different users get different recommendations now.
personalized_model.recommend(users=[users[0]])
personalized_model.recommend(users=[users[1]])
personalized_model.get_similar_items(['With Or Without You - U2'])
personalized_model.get_similar_items(['Chan Chan (Live) - Buena Vista Social Club'])
We now formally compare the popularity and the personalized models using precision-recall curves.
if graphlab.version[:3] >= "1.6":
model_performance = graphlab.compare(test_data, [popularity_model, personalized_model], user_sample=0.05)
graphlab.show_comparison(model_performance,[popularity_model, personalized_model])
else:
%matplotlib inline
model_performance = graphlab.recommender.util.compare_models(test_data, [popularity_model, personalized_model], user_sample=.05)